I am frequently asked how to learn data mining and data science. Here is my summary.

You can best learn data mining and data science by doing, so start analyzing data as soon as you can! However, don't forget to learn the theory, since you need a good statistical and machine learning foundation to understand what you are doing and to find real nuggets of value in the noise of big data.

Education: Watch webinars, take courses and consider a certificate or a degree in data science (Read more in Ben Lorica's How to Nurture a Data Scientist.)

Data: Check available data resources and find something there

Competitions: Participate in data mining competitions

Interact with other data scientists, via social networks, groups and meetings

In this article, I use data mining and data science interchangeably. See my presentation, Analytics Industry Overview, where I look at the evolution and popularity of different terms like statistics, knowledge discovery, data mining, predictive analytics, data science and big data.

2. Tools: Data Mining, Data Science, and Visualization Software

There are many data mining tools for different tasks, but it is best to learn how to use a data mining suite that supports the entire process of data analysis. You can start with open-source (free) tools such as KNIME, RapidMiner and Weka.

However, for many analytics jobs you need to know SAS, which is the leading commercial tool and widely used. Other popular analytics and data mining software include MATLAB, StatSoft STATISTICA, Microsoft SQL Server, Tableau, IBM SPSS Modeler, and Rattle.

Visualization is an essential part of any data analysis. Learn how to use Microsoft Excel (good for many simpler tasks), R graphics, (especially ggplot2), and also Tableau - an excellent package for visualization. Other good visualization tools include TIBCO Spotfire and Miner3D.

3. Textbooks

There are many data mining and data science textbooks available, but you can check these:

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Gregory Piatetsky-Shapiro, Ph.D. is the President of KDnuggets, which provides analytics and data mining consulting. Gregory is a founder of KDD (Knowledge Discovery and Data mining conferences) and is one of the leading experts in the field.